Apache Beam is a unified programming model for expressing efficient and portable data processing pipelines. It provides a programming model and set of language-specific SDKs for building pipelines that can run on multiple distributed processing backends. Beam pipelines consist of transformations and collections that allow expressing data processing workflows in a way that is portable across different execution environments like Apache Spark, Apache Flink, and Google Cloud Dataflow. Examples of pipelines covered include word counting from text, trending hashtags on Twitter, and billing estimates.

1. Data Processing with
Apache Beam
Pydata Seattle 2017

2. I am Sourabh
Hello!

3. I am Sourabh
Hello!
I am a Software Engineer

4. I am Sourabh
Hello!
I am a Software Engineer
I tweet at @sb2nov

5. I am Sourabh
Hello!
I am a Software Engineer
I tweet at @sb2nov
I like Ice Cream

6. What is Apache Beam?

7. Apache Beam is a unified
programming model for
expressing efficient and
portable data processing
pipelines

8. Big Data

9. https://commons.wikimedia.org/wiki/File:Globe_centered_in_the_Atlantic_Ocean_(green_and_grey_globe_scheme).svg
LAUNCH!!

10. DATA CAN BE BIG

11. … REALLY BIG ...
Tuesday
Wednesday
Thursday

12. UNBOUNDED, DELAYED, OUT OF
ORDER
9:008:00 14:0013:0012:0011:0010:00
8:00
8:008:00

13. ORGANIZING THE STREAM
8:00
8:00
8:00

14. DATA PROCESSING TRADEOFFS
Completeness Latency
$$$
Cost

15. WHAT IS IMPORTANT?
Completeness Low Latency Low Cost
Important
Not Important
$$$

16. MONTHLY BILLING
Completeness Low Latency Low Cost
Important
Not Important
$$$

17. BILLING ESTIMATE
Completeness Low Latency Low Cost
Important
Not Important
$$$

18. FRAUD DETECTION
Completeness Low Latency Low Cost
Important
Not Important
$$$

19. Beam
Model

20. Pipeline
PTransform
PCollection
(bounded or
unbounded)

21. EVENT TIME VS PROCESSING TIME

22. ASKING THE RIGHT QUESTIONS
When in processing time?
What is being computed?
Where in event time?
How do refinements happen?

23. WHAT IS BEING COMPUTED?
scores: PCollection[KV[str, int]] = (input
| Sum.integersPerKey())

24. WHAT IS BEING COMPUTED?

25. WHERE IN EVENT TIME?
scores: PCollection[KV[str, int]] = (input
| beam.WindowInto(FixedWindows(2 * 60))
| Sum.integersPerKey())

26. WHERE IN EVENT TIME?

27. WHERE IN EVENT TIME?

28. WHEN IN PROCESSING TIME?
scores: PCollection[KV[str, int]] = (input
| beam.WindowInto(FixedWindows(2 * 60)
.triggering(AtWatermark()))
| Sum.integersPerKey())

29. WHEN IN PROCESSING TIME?

30. HOW DO REFINEMENTS HAPPEN?
scores: PCollection[KV[str, int]] = (input
| beam.WindowInto(FixedWindows(2 * 60)
.triggering(AtWatermark()
.withEarlyFirings(AtPeriod(1 * 60))
.withLateFirings(AtCount(1)))
.accumulatingFiredPanes())
| Sum.integersPerKey())

31. HOW DO REFINEMENTS HAPPEN?

32. CUSTOMIZING WHAT WHERE WHEN HOW
Classic
Batch
Windowed
Batch
Streaming Streaming +
Accumulation
For more information see https://cloud.google.com/dataflow/examples/gaming-example

33. Examples

34. WORD COUNT
http://www.levraphael.com/blog/wp-content/uploads/2015/06/word-pile.jpg

35. WORD COUNT
import apache_beam as beam, re

36. WORD COUNT
import apache_beam as beam, re
with beam.Pipeline() as p:
(p
| beam.io.textio.ReadFromText("input.txt"))

37. WORD COUNT
import apache_beam as beam, re
with beam.Pipeline() as p:
(p
| beam.io.textio.ReadFromText("input.txt")
| beam.FlatMap(lamdba s: re.split("W+", s)))

38. WORD COUNT
import apache_beam as beam, re
with beam.Pipeline() as p:
(p
| beam.io.textio.ReadFromText("input.txt")
| beam.FlatMap(lamdba s: re.split("W+", s))
| beam.combiners.Count.PerElement())

39. WORD COUNT
import apache_beam as beam, re
with beam.Pipeline() as p:
(p
| beam.io.textio.ReadFromText("input.txt")
| beam.FlatMap(lamdba s: re.split("W+", s))
| beam.combiners.Count.PerElement()
| beam.Map(lambda (w, c): "%s: %d" % (w, c)))

40. WORD COUNT
import apache_beam as beam, re
with beam.Pipeline() as p:
(p
| beam.io.textio.ReadFromText("input.txt")
| beam.FlatMap(lamdba s: re.split("W+", s))
| beam.combiners.Count.PerElement()
| beam.Map(lambda (w, c): "%s: %d" % (w, c))
| beam.io.textio.WriteToText("output/stringcounts"))

41. TRENDING ON TWITTER
http://thegetsmartblog.com/wp-content/uploads/2013/06/Twitter-trends-feature.png

42. TRENDING ON TWITTER
with beam.Pipeline() as p:
(p
| beam.io.ReadStringsFromPubSub("twitter_topic"))

43. TRENDING ON TWITTER
with beam.Pipeline() as p:
(p
| beam.io.ReadStringsFromPubSub("twitter_topic")
| beam.WindowInto(SlidingWindows(5*60, 1*60)))

44. TRENDING ON TWITTER
with beam.Pipeline() as p:
(p
| beam.io.ReadStringsFromPubSub("twitter_topic")
| beam.WindowInto(SlidingWindows(5*60, 1*60))
| beam.ParDo(ParseHashTagDoFn()))

45. TRENDING ON TWITTER
with beam.Pipeline() as p:
(p
| beam.io.ReadStringsFromPubSub("twitter_topic")
| beam.WindowInto(SlidingWindows(5*60, 1*60))
| beam.ParDo(ParseHashTagDoFn())
| beam.combiners.Count.PerElement())

46. TRENDING ON TWITTER
with beam.Pipeline() as p:
(p
| beam.io.ReadStringsFromPubSub("twitter_topic")
| beam.WindowInto(SlidingWindows(5*60, 1*60))
| beam.ParDo(ParseHashTagDoFn())
| beam.combiners.Count.PerElement()
| beam.ParDo(BigQueryOutputFormatDoFn())
| beam.io.WriteToBigQuery("trends_table"))

47. Portability
&
Vision
Google Cloud
Dataflow

48. Other
Languages
Beam
Java
Beam
Python Pipeline SDK
User facing SDK, defines a language
specific API for the end user to
specify the pipeline computation
DAG.

49. Runner API
Other
Languages
Beam
Java
Beam
Python Runner API
Runner and language agnostic
representation of the user’s pipeline
graph. It only contains nodes of Beam
model primitives that all runners
understand to maintain portability
across runners.

50. Runner API
Other
Languages
Beam
Java
Beam
Python
Execution ExecutionExecution
SDK Harness
Docker based execution
environments that are shared by all
runners for running the user code in a
consistent environment.

51. Fn API
Runner API
Other
Languages
Beam
Java
Beam
Python
Execution ExecutionExecution
Fn API
API which the execution
environments use to send and receive
data, report metrics around execution
of the user code with the Runner.

52. Fn API
Apache
Flink
Apache
Spark
Runner API
Other
Languages
Beam
Java
Beam
Python
Execution Execution
Cloud
Dataflow
Execution
Apache
Gear-
pump
Apache
Apex
Runner
Distributed processing environments
that understand the runner API
graph and how to execute the Beam
model primitives.

53. More Beam?
Issue tracker (https://issues.apache.org/jira/projects/BEAM)
Beam website (https://beam.apache.org/)
Source code (https://github.com/apache/beam)
Developers mailing list (dev-subscribe@beam.apache.org)
Users mailing list (user-subscribe@beam.apache.org)

54. Thanks!
You can find me at: @sb2nov
Questions?